MetaInsight

Network meta-analysis can help determine the ‘best’ treatment option

Complex reviews require extensive statistical and programming knowledge

BUGSnet, bnma, coda, gemtc, meta, metafor, netmeta
Some dependent on JAGS which may be hard to install
Require data in different formats and use different terminology

Shiny removes barriers for accessing cutting-edge methods

MetaInsight was launched in 2019 to make NMA more accessible
Originally developed by statisticians, but increasingly in collaboration with developers

The ‘black box’ nature of apps can limit uptake

{shinyscholar} was forked from {wallace} to make development of reproducible apps easier
Convert core functionality into functions and package
App becomes an interface to the functions, dealing with interactivity

Hex logo of shinyscholar showing a black and white mortar cap

Reproducibility relies on a strict structure

Each module has an id made up of the component and module e.g. summary_network
The module calls a synonymous function summary_network()
Input values are stored in common$meta$summary_network$<input id>
Values are knitted into an .Rmd chunk and combined to create a .qmd

Reproducibility relies on a strict structure

```{asis, echo = {{summary_network_knit}, eval = {{summary_network_knit}}, include = {{summary_network_knit}}}}
### Display the networks for the original data and data with excluded studies.
{r,  results = 'asis'}
```
```{r, echo = {{summary_network_knit}, include = {{summary_network_knit}}}}
summary_network(frequentist_all, 
                bugsnet_all, 
                {{summary_network_style}}, 
                {{summary_network_label_all}}, 
                "Network plot of all studies")
```

### Display the networks for the original data and data with excluded studies.

```{r, results = 'asis'}
summary_network(frequentist_all, 
                bugsnet_all, 
                "netplot", 
                1, 
                "Network plot of all studies")
```

Reproducibility also enables improved reporting

Use as the basis for writing a publication
Rendered in the app to produce an html report

Incorporating risk of bias scores improves sensitivity analyses

MetaInsight enables sensitivity analyses by excluding studies
During reviews, risk of bias information can be collected e.g.
Randomisation, blinding, missing data
Users can now upload these scores and use them to guide sensitivity analyses

Integration with CiNEMA helps to evaluate confidence in findings

Uses risk of bias scores for studies to evaluate evidence for treatments

Acknowledgments

Naomi Bradbury, Ryan Field, Tom Morris, Clareece Nevill, Janion Nevill, Alex Sutton, Nicola Cooper
Wellcome
NIHR
Chan Zuckerburg Initiative
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